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Resource management in databases allows administrators to have control over resources and assign a priority to sessions,...
03/06/2022

Resource management in databases allows administrators to have control over resources and assign a priority to sessions, ensuring the most critical transactions get a significant share of system resources. Resource management in a distributed environment makes accessibility of data more accessible and manages resources over the network of autonomous computers (i.e., Distributed systems). The basis of resource management in the distributed system is also resource sharing.

PrestoDB is a distributed query engine written by Facebook as the successor to Hive for the highly scalable processing of large volumes of data. Written for the Hadoop ecosystem, PrestoDB is built to scale to tens of thousands of nodes and process petabytes of data. To be usable at a production scale, PrestoDB was built to serve thousands of queries to multiple users without facing bottle-necking and “noisy neighbor” issues. PrestoDB makes use of resource groups in order to organize how different workloads are prioritized. This post discusses some of the paradigms that PrestoDB introduces with resource groups, as well as best practices and considerations to think about before setting up a production system with resource grouping.

This post discusses some of the paradigms that PrestoDB introduces with resource groups as well as best practices.

ETL (extract, transform, load) has been a standard approach to data integration for many years. But the rise of cloud co...
02/06/2022

ETL (extract, transform, load) has been a standard approach to data integration for many years. But the rise of cloud computing and the need to integrate self-service data has led to the development of new methodologies such as ELT (extract, load, transform) and reverse ETL.

What are the advantages of ETL? How do these three data integration approaches differ? Is ETL preferable to ELT for your data pipeline use cases? Why and when is reverse ETL valuable for your data warehouse (DW) and a data lake? Why is reverse ETL not an optional choice for data pipeline ETL or ELT, but rather another process opportunity for a DW and data lake?

To help you choose the data integration method for your data pipeline projects, we briefly explore ETL and ELT — their strengths and weaknesses and how to exploit both technologies. We describe why ETL is an exceptional choice if you need to transform to support business logic, granular compliance on data in flight, and in the case of ETL streaming, low latency. We also explore how ELT is a better option for those who need fast data loading, minimized maintenance, and highly automated workflows.

What are the advantages of ETL? Is ETL preferable to ELT for your data pipeline use cases? Why and when is reverse ETL valuable for your data warehouse (DW)?

LG optimise le principe du double écran pour travailler
23/05/2022

LG optimise le principe du double écran pour travailler

Le nouveau moniteur PC de la marque permet de loger un grand écran sur un minimum de place.

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